code: def test_fit_performs_dataset_compression_without_precision_when_int(
      dataset_compression: Dict,
      make_automl: Callable[..., AutoML],
  ) -> None:
      """
      Parameters
      ----------
      dataset_compression: Dict
          The dataset_compression arg with "precision" set in it
  
      Fixtures
      --------
      make_automl: Callable[..., AutoML]
          Makes an automl instance
  
  
      Expects
      -------
      * Should call reduce_dataset_size_if_too_large
      * "precision" should have been removed from the "methods" passed to the keyword
          argument "operations" of `reduce_dataset_size_if_too_large`.
  
      Note
      ----
      * Only done with int's as we can't reduce precision of ints in a meaningful way
      """
      X = np.ones((100, 10), dtype=int)
      y = np.random.random((100,))
  
      auto = make_automl(dataset_compression=dataset_compression)
  
      with patch(
          "autosklearn.automl.reduce_dataset_size_if_too_large",
          return_value=(X, y),
      ) as mck:
          # To prevent fitting anything we use `only_return_configuration_space`
          auto.fit(
              X,
              y,
              only_return_configuration_space=True,
              task=BINARY_CLASSIFICATION,
          )
  
      assert mck.call_count == 1
  
      args, kwargs = mck.call_args
      assert kwargs["operations"] == ["subsample"]
  
file_path: auto-sklearn/test/test_automl/test_dataset_compression.py
imports: from typing import Any, Callable, Dict
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from autosklearn.automl import AutoML
  from autosklearn.constants import BINARY_CLASSIFICATION
  from pytest_cases import parametrize
  from unittest.mock import patch
  from test.util import skip
label_desc: Use image node_id 1 for calling a global function with example usage: test_fit_performs_dataset_compression_without_precision_when_int(dataset_compression, make_automl) without return types